A topic trend can be inferred by the usage of tags -- we name it as attention. Time-series analysis for tagging prediction can indicate the evolution of attention flow. This side takes political analysis for example, using time-series technique and discover interesting patterns.
Attention Flow ByTagging prediction Time-Series Data Analysis Yong Zheng and Mengran Liu
Introduction• Flow of Attention (Popular Attention) Attentions in our life Stars: movie star, music star, sports star Media: Harry Potter New Tech: iPad3, Canon 5D III Rumor: iPhone 4S v.s. iPhone 5 Attentions on the Internet Social Networks: Facebook, Twitter, MySpace Social Media: Netflix, Pandora, Flickr Social Bookmarking: Delicious.com, Diigo.com
Conclusions• The forecasting is not that bad. We can catch the peaks! Whether the model can predict the bursts is still under discussion. Data is limited – it would be better if we have data within more years.• The model includes both AR and MA components, where AR is relevant to Long Term effect and MA relies on Short Term memory. It makes sense if the topic is “Bush”.
Future Research• Use Bush data to predict Obama data; presidents usually experience similar activities, such as election, national congress or meetings, etc. Tag usage may show time-series effects. Political trend is one interesting research area.• It can be extended and applied to other topics/tags, like “iPad3”, “Thanks Giving’s” which may show seasonal effects.• Further Research: Topic Detection & Evolutions